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# Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport
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This code provides a PyTorch implementation for TRA (Temporal Routing Adaptor), as described in the paper [Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport](http://arxiv.org/abs/2106.12950).
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Temporal Routing Adaptor (TRA) is designed to capture multiple trading patterns in the stock market data. Please refer to [our paper](http://arxiv.org/abs/2106.12950) for more details.
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* TRA (Temporal Routing Adaptor) is a lightweight module that consists of a set of independent predictors for learning multiple patterns as well as a router to dispatch samples to different predictors.
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* We also design a learning algorithm based on Optimal Transport (OT) to obtain the optimal sample to predictor assignment and effectively optimize the router with such assignment through an auxiliary loss term.
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If you find our work useful in your research, please cite:
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```
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@inproceedings{HengxuKDD2021,
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author = {Hengxu Lin and Dong Zhou and Weiqing Liu and Jiang Bian},
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title = {Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport},
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booktitle = {Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
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series = {KDD '21},
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year = {2021},
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publisher = {ACM},
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}
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@article{yang2020qlib,
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title={Qlib: An AI-oriented Quantitative Investment Platform},
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author={Yang, Xiao and Liu, Weiqing and Zhou, Dong and Bian, Jiang and Liu, Tie-Yan},
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journal={arXiv preprint arXiv:2009.11189},
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year={2020}
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}
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```
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# Running TRA
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## Usage (Recommended)
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## Requirements
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- Install `Qlib` main branch
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**Update**: `TRA` has been moved to `qlib.contrib.model.pytorch_tra` to support other `Qlib` components like `qlib.workflow` and `Alpha158/Alpha360` dataset.
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## Running
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Please follow the official [doc](https://qlib.readthedocs.io/en/latest/component/workflow.html) to use `TRA` with `workflow`. Here we also provide several example config files:
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- `workflow_config_tra_Alpha360.yaml`: running `TRA` with `Alpha360` dataset
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- `workflow_config_tra_Alpha158.yaml`: running `TRA` with `Alpha158` dataset (with feature subsampling)
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- `workflow_config_tra_Alpha158_full.yaml`: running `TRA` with `Alpha158` dataset (without feature subsampling)
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The performances of `TRA` are reported in [Benchmarks](https://github.com/microsoft/qlib/tree/main/examples/benchmarks).
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## Usage (Not Maintained)
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This section is used to reproduce the results in the paper.
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### Running
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We attach our running scripts for the paper in `run.sh`.
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And here are two ways to run the model:
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* Running from scripts with default parameters
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You can directly run from Qlib command `qrun`:
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```
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qrun configs/config_alstm.yaml
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```
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You can directly run from Qlib command `qrun`:
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```
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qrun configs/config_alstm.yaml
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```
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* Running from code with self-defined parameters
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Setting different parameters is also allowed. See codes in `example.py`:
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```
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python example.py --config_file configs/config_alstm.yaml
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```
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Setting different parameters is also allowed. See codes in `example.py`:
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```
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python example.py --config_file configs/config_alstm.yaml
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```
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Here we trained TRA on a pretrained backbone model. Therefore we run `*_init.yaml` before TRA's scipts.
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# Results
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## Outputs
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### Results
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After running the scripts, you can find result files in path `./output`:
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`info.json` - config settings and result metrics.
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* `info.json` - config settings and result metrics.
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* `log.csv` - running logs.
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* `model.bin` - the model parameter dictionary.
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* `pred.pkl` - the prediction scores and output for inference.
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`log.csv` - running logs.
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Evaluation metrics reported in the paper:
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`model.bin` - the model parameter dictionary.
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`pred.pkl` - the prediction scores and output for inference.
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## Our Results
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| Methods | MSE| MAE| IC | ICIR | AR | AV | SR | MDD |
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|-------------------|-------------------|---------------------|--------------------|--------------------|--------------------|--------------------|--------------------|--------------------|
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|-------|-------|------|-----|-----|-----|-----|-----|-----|
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|Linear|0.163|0.327|0.020|0.132|-3.2%|16.8%|-0.191|32.1%|
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|LightGBM|0.160(0.000)|0.323(0.000)|0.041|0.292|7.8%|15.5%|0.503|25.7%|
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|MLP|0.160(0.002)|0.323(0.003)|0.037|0.273|3.7%|15.3%|0.264|26.2%|
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@@ -61,21 +85,8 @@ After running the scripts, you can find result files in path `./output`:
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A more detailed demo for our experiment results in the paper can be found in `Report.ipynb`.
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# Common Issues
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## Common Issues
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For help or issues using TRA, please submit a GitHub issue.
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Sometimes we might encounter situation where the loss is `NaN`, please check the `epsilon` parameter in the sinkhorn algorithm, adjusting the `epsilon` according to input's scale is important.
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# Citation
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If you find this repository useful in your research, please cite:
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```
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@inproceedings{HengxuKDD2021,
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author = {Hengxu Lin and Dong Zhou and Weiqing Liu and Jiang Bian},
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|
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title = {Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport},
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booktitle = {Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery \& Data Mining},
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series = {KDD '21},
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year = {2021},
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publisher = {ACM},
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}
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```
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Sometimes we might encounter situation where the loss is `NaN`, please check the `epsilon` parameter in the sinkhorn algorithm, adjusting the `epsilon` according to input's scale is important.
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Dong Zhou